From 595ca841fc1c40090b969919003b1fc0d438b8f6 Mon Sep 17 00:00:00 2001
From: IamTingTing <6121smile@gmail.com>
Date: Fri, 18 Jul 2025 21:06:39 +0800
Subject: [PATCH] fix: dynamic input dim in DiffusionModelEncoder

Signed-off-by: IamTingTing <6121smile@gmail.com>
---
 monai/networks/nets/diffusion_model_unet.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/monai/networks/nets/diffusion_model_unet.py b/monai/networks/nets/diffusion_model_unet.py
index 11196bb343..ad05f6dd6a 100644
--- a/monai/networks/nets/diffusion_model_unet.py
+++ b/monai/networks/nets/diffusion_model_unet.py
@@ -33,6 +33,7 @@
 
 import math
 from collections.abc import Sequence
+from typing import Optional
 
 import torch
 from torch import nn
@@ -2005,7 +2006,7 @@ def __init__(
 
             self.down_blocks.append(down_block)
 
-        self.out = nn.Sequential(nn.Linear(4096, 512), nn.ReLU(), nn.Dropout(0.1), nn.Linear(512, self.out_channels))
+        self.out: Optional[nn.Module] = None
 
     def forward(
         self,
@@ -2048,6 +2049,12 @@ def forward(
             h, _ = downsample_block(hidden_states=h, temb=emb, context=context)
 
         h = h.reshape(h.shape[0], -1)
+
+        # 5. out
+        if self.out is None:
+            self.out = nn.Sequential(
+                nn.Linear(h.shape[1], 512), nn.ReLU(), nn.Dropout(0.1), nn.Linear(512, self.out_channels)
+            )
         output: torch.Tensor = self.out(h)
 
         return output